AIQTrees: a drone imagery dataset for tree segmentation

Chai, Joseph; To, Alex; O’Sullivan, Barry; Nguyen, Hoang D.

AIQTrees: a drone imagery dataset for tree segmentation

Files

AIQTrees_Paper (003).pdf(4.4 MB)

Accepted Version

Date

2024

Authors

Chai, Joseph

To, Alex

O’Sullivan, Barry

Nguyen, Hoang D.

Abstract

The reliability of AI models typically depends on the data they are trained with, and accurate interpretations require large amounts of data. The scarcity of publicly available datasets is typically encountered for specific small-scale sustainability projects, making data accessibility a limiting factor for developing AI models for semantic segmentation tasks. In sustainability and forestry applications, the usage of UAVs is common due to their lightweight nature and the ability to provide a huge variety of data. In this paper, we present a new dataset of realistic and high-quality drone images taken around sites in Ireland. The images encompass temporal, spatial, and seasonal dimensions, which could alter the tree appearance or illumination conditions of the images and have to be taken into consideration. We also included a baseline benchmark for the semantic segmentation task along with the dataset. It can be accessed at: https://github.com/ReML-AI/AIQTrees.

Keywords

Public dataset , Semantic segmentation , Sustainability , Drone imagery

Citation

Chai, J., To, A., O’Sullivan, B. and Nguyen, H. D. (2024) 'AIQTrees: a drone imagery dataset for tree segmentation', Reliable and Trustworthy Artificial Intelligence Workshop at the 16th Asian Conference on Machine Learning (ACML 2024), Hanoi, Vietnam, 5-8 December 2024.